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Copyright of China Rubber Industry is the property of Editorial Office of China Rubber Industry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of International Journal of Engineering Design & Technology (IJEDT) is the property of Burdur Mehmet Akif Ersoy University, Faculty of Architecture & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, in order to reduce the indentation rolling resistance of a belt conveyor and improve its operation efficiency, the indentation rolling resistance of a belt conveyor is studied. Firstly, the theoretical calculation of the indentation rolling resistance caused by the contact between a conveyor belt and an idler is studied. Additionally, the expression of the indentation rolling resistance including the multi-element Maxwell relaxation modulus is simplified and deduced using the assumption of small deformation, and the steps of iterative calculation of the indentation rolling resistance through setting the initial values are designed. Then, the multi-component Maxwell relaxation modulus function of the conveyor belt tested in the previous work of the authors is substituted into COMSOL 5.6 finite element software, and the stress distribution caused by the contact between the conveyor belt and the idler under different working conditions is analyzed. Also, a series of measures to reduce the indentation rolling resistance are found through the laws presented by the simulation results. Finally, the experimental study of the indentation rolling resistance is carried out using the indentation rolling resistance testing device from our research group. By comparing the experimental results for the indentation rolling resistance under different working conditions with the theoretical calculation results for the indentation rolling resistance, it was found that the relative error was less than 13%, which proved the feasibility of the indentation rolling resistance and relaxation modulus testing method, as well as the theoretical calculation method for indentation rolling resistance. [ABSTRACT FROM AUTHOR]

The problem of achieving a good maintenance plan is well-known in the modern industry. One of the most promising approaches is predictive maintenance, which schedules interventions based on predictions made by collecting and analyzing data from the process. However, to the best of the authors' knowledge, this approach is still not widespread and known enough, and particularly, the real-case scenarios of its application appear not exhaustive. To contribute to fill this gap, this work proposes a digital twin (DT), which performs a predictive maintenance approach for a conveyor belt within a real-case scenario with the overall goal of predicting faults during normal belt operations. Specifically, the core of the implemented DT is a model that analyzes the data collected by various sensors distributed along the conveyor belt. In turn, this model exploits a machine learning-based algorithm that predicts the insurgence of faults. The tests of the developed solution, conducted within a real scenario, demonstrated good precision and accuracy in identifying the fault status and also in a time deemed acceptable for the involved stakeholders. [ABSTRACT FROM AUTHOR]

AbstractThis study primarily aimed to trace the linkages between the Great Ocean Conveyor Belt [originating from the North Atlantic] and the “Great” Colonialism [originating from Portugal]. Consequently, the study adopted the qualitative documentary method to observe, scrutinize, and interpret the linkages between the Ocean Conveyor Belt and Colonialism. The findings show that India lies along the North Indian Ocean in South Asia [i.e. right above the point where the conveyor’s lower limb (its subsidiary) first U-turns to loot heat] where the Portuguese first initiated the process after their arrival in 1498, while China lies along the coast of the North Pacific Ocean in East Asia [i.e. the second point where the conveyor’s lower limb later U-turns to loot heat] where the colonizers later extended their expansion to and, then, U-turned to annex lands, loaded ships with goods, and cruised back to Europe. However, the conveyor’s lower and upper limbs deliver, inter alia, processed materials and wars to the South and raw materials and race struggles to the North, respectively. The North–South Conveyor Belt Model, thus, maintains that the conveyor’s upper and lower limbs superimpose positive and negative effects on the Global North and South, respectively.

Conveyor belt is one of the most important transportation equipment in underground coal mines. Anchor rod, channel steel, large gangue, and other foreign objects that are mixed with the conveyor belt during conveying operations cause the belt to tear, clog the coal drop opening, and cause other serious safety incidents. They seriously affect the efficiency of transportation and even threaten the lives of workers. Aiming at the existing conveyor belt foreign object detection algorithm’s problems such as weak characterization of slender objects and poor weak semantic feature extraction, a foreign object detection algorithm based on low-level feature enhancement with Transformer is designed, notated as FET–YOLO. Firstly, to address the problem that existing detection networks have difficulty in extracting features of elongated objects, deformable convolution is introduced to enhance the network’s adaptability to the shape characteristics of elongated foreign objects, and the MobileViT module is used to increase the differentiation between the foreign objects and the background in the image, in order to extract features that are more consistent with the diversity of the elongated foreign objects and to weaken the interference of background noise.Secondly, constructing a low-level feature enhancement module LFEM, to improve the representation of weak semantic features of foreign objects in the detection network in order to reduce the probability of wrong detection.Finally, the introduction of gsconv reduces the information loss due to changes in the size of the feature map and ensures that the network extracts features efficiently while reducing the number of model parameters. The training set and validation set are produced by using the video of conveyor belt work in an underground coal mine. The proposed algorithm is compared to three other conveyor belt detection algorithms, and the experimental results show that the proposed algorithm can better solve the problems of poor detection of elongated objects and difficulty in weak semantic feature extraction in conveyor belt foreign object target detection, with higher detection accuracy. For images with a resolution size of 640×640, the performance metrics mAP@0.5 can be up to 0.875, mAP@0.5:0.95 can be up to 0.543, and the detection speed is 75 fps.

Steel cord conveyor belts are widely used in long-distance transportation of various materials. The steel cord belt is composed of rubber and steel cord core, exhibiting excellent viscoelasticity and complex dynamic characteristics during operation. The existing static elastic modulus cannot accurately reflect the real dynamic properties of the conveyor belt. In this paper, based on the catenary theory, the formula for the dynamic elastic modulus of the conveyor belt is derived by introducing the sag and elastic deformation of the steel cord conveyor belt on the basis of the static elastic modulus. The dynamic modeling of the steel cord conveyor belt is carried out using rigid belt block element + flexible beam method. Then, the RecurDyn software is used to establish the dynamic simulation system of the belt conveyor and conduct simulations, which are validated through a test bench. The results show that dynamics simulation based on the dynamic elastic modulus is closer to the real dynamic characteristics of the belt conveyor. Finally, the dynamic characteristics of belt conveyors under different start-up times, different start-up speeds, and idler spacing are analyzed using this modeling and simulation method. The results demonstrate that the dynamic elastic modulus can accurately reflect the real dynamic properties of the conveyor belt, providing a more accurate dynamic modeling and simulation method for the design of belt conveyors. [ABSTRACT FROM AUTHOR]

Conveyor belts are used for material transportation in the mining and construction industries. The friction and wear properties of the styrene butadiene rubber (SBR) used in conveyor belts are investigated, including the effects of the applied load and rotational speed on the friction coefficient and weight loss. The sliding wear mechanism of the SBR is analyzed using scanning electron microscope, energy dispersive spectrometer, and Fourier transform infrared spectrometer. The results demonstrate that the friction coefficient and total weight loss of SBR increase as a function of rotational speed, whereas the friction coefficient decreases and the total weight loss increases as a function of increasing applied load. The maximum friction coefficient occurred at a loading of 10 N and a speed of 120 revolutions·min−1, and the maximum total weight loss occurred at a loading of 20 N and a speed of 120 revolutions·min−1. In comparison to the unworn surface, the worn surface of the SBR exhibits melting, adhesive, and abrasive wear characteristics. The increases in the hardness, yellowing, oxygen content, and the number of complex structures, such as hydroxyl, aldehyde, ketone, and ether groups, occur simultaneously. [ABSTRACT FROM AUTHOR]

Copyright of Duzce University Journal of Science & Technology is the property of Duzce University Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Coal Science & Technology (0253-2336) is the property of Coal Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The key issue for ensuring economic efficiency and continuous operation of conveyor transport is the recognition of the condition of the belt core. Faults in steel cords in the core are not visible during routine visual inspections, but they can be identified using magnetic diagnostic systems such as DiagBelt+. The article presents an analysis of the impact of the sensitivity threshold of the DiagBelt+ system, the diameter of cords in the core, and the belt speed on the quality of signals representing known damage: cutting of cords, their absence, and a reduction in the cross-section of the cord. The study focuses on defects to cords across the belt, as they can weaken the belt's strength and lead to a complete belt failure. The proposed results and analyses contribute to the improvement of the methodology for magnetic examination of the core's condition and the developed diagnostic system DiagBelt+. Consequently, this enhances the reliability and safety of belt conveyors in various industries, including brown coal mines where it has been implemented (PGE GiEK SA KWB O/Bełchatów), as well as in hard coal, limestone, and copper ore mines where it is used to assess the condition of belts with steel cords. [ABSTRACT FROM AUTHOR]

Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation. [ABSTRACT FROM AUTHOR]

In this paper, in order to reduce the indentation rolling resistance of a belt conveyor and improve its operation efficiency, the indentation rolling resistance of a belt conveyor is studied. Firstly, the theoretical calculation of the indentation rolling resistance caused by the contact between a conveyor belt and an idler is studied. Additionally, the expression of the indentation rolling resistance including the multi-element Maxwell relaxation modulus is simplified and deduced using the assumption of small deformation, and the steps of iterative calculation of the indentation rolling resistance through setting the initial values are designed. Then, the multi-component Maxwell relaxation modulus function of the conveyor belt tested in the previous work of the authors is substituted into COMSOL 5.6 finite element software, and the stress distribution caused by the contact between the conveyor belt and the idler under different working conditions is analyzed. Also, a series of measures to reduce the indentation rolling resistance are found through the laws presented by the simulation results. Finally, the experimental study of the indentation rolling resistance is carried out using the indentation rolling resistance testing device from our research group. By comparing the experimental results for the indentation rolling resistance under different working conditions with the theoretical calculation results for the indentation rolling resistance, it was found that the relative error was less than 13%, which proved the feasibility of the indentation rolling resistance and relaxation modulus testing method, as well as the theoretical calculation method for indentation rolling resistance.

The development of high-performance rubber composites has always been a research hotspot in the field of conveyor belt manufacturing. In this work, a rubber cover joint composite made of reduced graphene oxide (rGO) was prepared using latex mixing and mechanical blending methods, with a steel wire rope conveyor belt as the research object, and the influence of the rGO content on the properties of the rubber composite is discussed. The structure and morphology characterization of the rGO/NR rubber show that the addition of rGO does not change its crystal structure, and 1.2 phr rGO is uniformly dispersed throughout the rubber composite. As more rGO is added, the mechanical properties of the rGO rubber cover joint first improve and then worsen. With the addition of 1.2 phr, the cross-linking density increases by 80.6%, the tensile strength of the rubber composites increases by 49.7%, the elongation at break increases by 23.6%, and the adhesion strength increases by 12.4%. The tensile strength of the rGO rubber cover joint can still maintain 72.5% of its pre-thermal aging value. The wear resistance and thermal conductivity increase as more phr is added. When 3.0 phr is added, the wear resistance of the rubber composites increases by 32.9%, the thermal conductivity increases by 118.8%, and the temperature difference at the completion of vulcanization decreases from 4.5 °C to 1.8 °C. The results show that when 1.2 phr of rGO is added, the rubber conveyor belt joint obtains the best comprehensive performance. These enhanced comprehensive properties allow for the practical application of rGO nanomaterials to conveyor belt rubber. [ABSTRACT FROM AUTHOR]

Belt conveyors are one type of the most important transportation devices for continuous transportation of bulk materials in bulk cargo piers, whose operating environments are complicated. The belt damages change in a wide scale range and multiple scales coexist. Thus, the reliability of damage detection methods is challenging. A Yolov5-based improved EMA-YOLO belt multi-scale damage detection method is put forward here. First, a high-efficiency multi-scale attention module EMA is introduced in view of there being complex background interferences in belt damage detection samples so that important areas shall be more concerned in our model. The multi-scale features can be extracted at different levels and the resistance ability of background interferences may be enhanced. Additionally, the feature fusion network is improved. The weight of learning is introduced by means of a simple and efficient weighted bidirectional feature pyramid network (BiFPN) to learn the weights of different input feature layers and modular repeated application is performed so that our model can simply and quickly fuse multi-scale features. Thus, the training performance of our model can be improved. Our measurements indicate that the accuracy of our detection method is up to 98.51%, and its detection speed is up to 82.26 FPS. Compared with the existing methods, our method can be more reliable and real time. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Test & Measurement Technology is the property of Publishing Center of North University of China and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The video game Factorio is a popular real-time strategy game that hosts a variety of gameplay scenarios analogous to real-world problems such as factory building, pipeline optimization, and conveyor belt logistics. It also considers environmental costs such as pollution and energy consumption. This game focuses on automation and resource management within the context of creating and maintaining factories. Players are challenged to design efficient factories, automate production, control energy consumption, and balance resource utilization, all while defending against environmental challenges such as pollution and hostile enemies. Factorio is a complex software; however, studies have demonstrated its ability to enhance student satisfaction when integrated as an educational module at the university level. This paper aims to utilize the power of Petri Net theory and the practical tool GPenSIM to model and simulate several Factorio pipelines and score them against one another, exploring which pipeline configuration is optimal. The primary objective is to make a mathematical model (Petri Net) to better understand the game's complex mechanisms, identify bottlenecks within the system, and explore opportunities for optimization within the network of modules. The insights gained from this paper may find practical applications in real-world scenarios. [ABSTRACT FROM AUTHOR]

The initial set of four measurements (scans) captures the failures occurring in the core of steel cable conveyor belt segments used in a lignite coal mine in Poland. These measurements utilize the DiagBelt+ magnetic system, enabling the analysis of averaged damage sizes. The scanning results allowed the analysis of complete changes in the failure sizes to a maximum of six different time intervals from 1 to 19 months. The cumulative increase in the length and width of the failure (their total extent in both directions) is a linear function of time. However, the inclination of the selected lines (growth speed) depends on the age of the belt segment at the time of its first scan. It is possible to find the best regression function of slope coefficients for the age of the belt segment. The obtained results are crucial for rational belt management, including reliable short-term forecasts of belt needs with the flow of time, indications of the best moments for belt segment replacements, and estimating the budget of the belting department. [ABSTRACT FROM AUTHOR]

The complex environmental influences often complicate the detection of longitudinal tears in conveyor belts, resulting in insufficient detection accuracy, overlooked detection, and elevated false detection rates. In this study, we propose a new depth learning method specifically designed for detecting longitudinal tears in conveyor belts. This method employs a linear Charge-Coupled Device (CCD) camera to capture images of the conveyor belt. These images are subsequently processed with a modified version of You Only Look Once (YOLO)v7 model to identify instances of longitudinal tearing. The modified YOLOv7 model features Efficient Intersection over Union (EIoU) loss function as a substitute for the original loss function. Furthermore, a Simple Parameter-Free Attention Module (SimAM) is introduced in the detection head to improve detection accuracy. In this method, we introduced the SimSPPFCSPC module as a new spatial pyramid pooling model. This module enhances detection speed while maintaining detection accuracy. Experiment results demonstrate the effectiveness of the proposed method, achieving an impressive precision of 94.6% and a detection speed of approximately 110 Frames Per Second (FPS). Such accuracy and speed meet the requirements for online detection of longitudinal tearing in belt conveyors.

This article presents an analysis of the transverse profile of belt damage in the context of the proper loading and operation of conveyors. The aim of this study was to identify and understand the characteristic features of damage and their placement that may occur in conveyor belts during operation and indicate abnormalities in any of the components of the conveyor system, especially during loading at chutes. A total of seven different conveyors were examined, and the obtained results allowed for a thorough comparison and investigation of the distribution of belt damage on their cross-sectional profiles. This article discusses factors that may contribute to the occurrence of unevenly distributed belt damage. The conclusions from the conducted research can be of significant importance for conveyor owners and operators as they enable an effective assessment of the conveyor belt's condition, the correctness of the conveyors, and the chutes' designs as well as the implementation of necessary design changes, correct actions, and repairs. Through proper belt monitoring and maintenance, the risk of failure can be minimized, extending the belts' lifespan and ensuring the efficiency of the transportation process. The article presents practical approaches to the analysis of the cross-sectional profile of damage, serving as a valuable source of information for individuals interested in optimizing the transportation process and maintaining efficient and safe conveyor operation. [ABSTRACT FROM AUTHOR]

In this work, the impact of vulcanization parameters on the adhesion strength of conveyor belts was investigated. Multi-ply textile-reinforced conveyor belts were produced at different vulcanization temperatures and times. The glass transition and melting temperature of fibers in the textile carcasses and the trend in vulcanization parameters adopted at conveyor belt-producing companies were considered in determining the vulcanization parameters. Post-vulcanization, the adhesion strength between the plies of the conveyor belt was thoroughly investigated. The study revealed that the adhesion strength of the conveyor belt relies on the vulcanization temperature and time. For the experiment conducted in this work, the optimum adhesion strength of the conveyor belt was found to be 9.9 N/mm at a vulcanization temperature of 160°C for 35 minutes. [ABSTRACT FROM AUTHOR]

To resolve the inaccurate localization of conveyor belt surface damage identification problem and to address the insufficiencies of the methods for extracting surface characterization information, this paper proposes a conveyor belt characterization information extraction method that integrates YOLOv5 deep learning and the skeleton method. By constructing a conveyor belt surface damage recognition model based on the YOLOv5 target detection algorithm, the identification, localization and cropping of the conveyor belt's surface damage are implemented. After that, edge extraction and surface information extraction are also performed on the damaged parts. Finally, the collected data are analyzed and processed in real time by edge computing equipment to determine the degree of damage of the parts. Finally, intelligent operation of the belt conveyor is achieved with autonomous operations, unattended operations and decision alarms. The experimental results show that the recognition accuracy of YOLOv5 is approximately 93.11%, the speed is approximately 57 frames per second and the error of the data acquired by image processing is between 2% and 10%, which meets the real-time detection requirements of conveyor belt surface damage detection, and assists in the safety management supervision of the belt conveyer. [ABSTRACT FROM AUTHOR]

Conveyor belts serve as the primary mode of ore transportation in mineral processing plants. Feeders, comprised of shorter conveyors, regulate the material flow from silos to longer conveyor belts by adjusting their velocity. This velocity manipulation is facilitated by automatic controllers that gauge the material weight on the conveyor using scales. However, due to positioning constraints of these scales, a notable delay ensues between measurement and the adjustment of the feeder speed. This dead time poses a significant challenge in control design, aiming to prevent oscillations in material levels on the conveyor belt. This paper contributes in two key areas: firstly, through a simulation-based comparison of various control techniques addressing this issue across diverse scenarios; secondly, by implementing the Smith predictor solution in an operational plant and contrasting its performance with that of a single PID controller. Evaluation spans both the transient flow rate during step change setpoints and a month-long assessment. The experimental results reveal a notable increase in production by 355 t/h and a substantial reduction in flow rate oscillations on the conveyor belt, evidenced by a 55% decrease in the standard deviation.

Conveyor belts (CBs) are widely used for the continuous transport of bulk materials. CBs must be extremely reliable due to the cost associated with their failure in continuous production systems. Thus, it is highly relevant in terms of maintenance and planning to find solutions to reduce the existing stoppages from these assets. In this sense, it is essential to monitor and collect real-time data from this piece of equipment. This work presents a case study, where a model that combines the Lean Philosophy, Total Productive Maintenance (TPM), and the enabling technologies of Industry 4.0 is applied to a CB. The proposed model monitors the CB and provides data on its operation, which, using the calculation of indicators, allows a more accurate and thorough view and evaluation, contributing to improving and supporting decision making by those responsible for maintenance. The data collected by the sensor help those responsible for maintenance and production, in the readjustment of more accurate and optimized planning, programming, and execution, supporting decision making in these areas. During the field test of a two-hour monitoring period (10 a.m. to 12 p.m.), the model identified six stoppages, resulting in approximately 88.6% of operational time for the conveyor. The field test showed that this model can result in more accurate maintenance decision making than conventional approaches. This research also contributes to the advancement of electronics and industrial automation sectors by empowering companies to transform maintenance methodologies. The potential of this approach and its implications for enhanced productivity and overall performance are therefore highlighted. [ABSTRACT FROM AUTHOR]

To tackle the issues of high training expense, inadequate real-time functionality, and insufficient reliability in deep neural network-based visual detection of conveyor belt damage, which significantly limit the practical use of the detection model in industrial settings, this study proposes a novel YoloX-ECA damage detection method, based on the YoloX. Initially, the backbone network CSPDarknet is used for feature extraction, followed by invoking channel attention mechanism ECA on the enhance feature extraction network, which fuses multi-scale damage features and further amplifies the attention on damage target feature, facilitating the extraction of superior features to reinforce resistance to environmental interference. Additionally, the training strategy combines cross-domain transfer and intra-domain transfer learning to improve the training performance and robustness of the model. The experimental results demonstrate that YoloX-ECA has a detection accuracy of 95.65% and a speed of 30.50 fps. Moreover, compared to existing methods, the detection performance of the proposed method is effectively upgraded, resulting in a balanced accuracy and rate of conveyor belt damage detection. [ABSTRACT FROM AUTHOR]

The foreign object doped in the conveying belt is the most important factor to cause the tearing of the conveying belt. In order to solve the problem of low accuracy and poor real-time performance of foreign object detection, a new method based on improved Nanodet is proposed in this paper. The hardware of conveyor belt foreign object detection system is designed with ARM processor, and the system software is designed based on Android. It uses a conveyor belt foreign object detection system to detect foreign object. In order to detect foreign object images, a better Nanodet model is suggested. In order to increase detection accuracy while preserving processing speed, the model uses SIoU in place of the original position loss function. When the enhanced Nanodet model is applied to the conveyor belt foreign object detection system, the image of the foreign object appearing on the conveyor belt can be identified. The experimental findings indicate that a conveyor belt foreign object detection system based on an ARM processor and an Android operating system is capable of detecting foreign objects on conveyor belts with an average detection accuracy of 94.3%, a detection speed of 30 frames per second. The application of this method in the detection of foreign objects in conveyor belt can solve the shortcomings of existing methods. At the same time meet the requirements of the conveyor belt foreign object detection site environment.

The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles’ surfaces. The proposed prototype’s reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants.

As the global market becomes increasingly competitive and demanding, companies face the challenge of responding swiftly and efficiently to customer needs. To maintain a competitive advantage, organisations must optimise the usage of their assets. This study focuses on the critical role of maintenance management and presents a novel, cost-effective, and easily applicable model that integrates Industry 4.0 (I4.0) and Total Productive Maintenance (TPM) principles to enhance production processes. The proposed model incorporates a real-time monitoring system equipped with sensors, a gateway, and Internet of Things (IoT) services. These components enable data acquisition, transmission, storage, and visualisation through both mobile and fixed devices. The model's effectiveness was validated through its implementation on a conveyor belt in a feed mill. The availability of the conveyor belt was around 89.5% before TPM implementation. After the implementation of TPM, it was possible to observe that the availability of the treadmill increased to 92.7% in a workweek of 105 h. On the other hand, it was possible to observe a considerable improvement in terms of the average time of each intervention, with a reduction of around 53%. Moreover, the model also facilitated improvements in the maintenance plan for the conveyor belt. Operators were trained to effortlessly comply with the new autonomous maintenance plan, enhancing operational efficiency. The joint application of I4.0 and TPM demonstrated significant advancements in maintenance management. The flexibility of the developed model allows for easy adaptation to monitor other condition parameters, enabling real-time decision-making for both the maintenance and production departments. This integrated approach holds the potential to drive process optimisation, ultimately contributing to improved productivity, cost-effectiveness, and competitiveness. [ABSTRACT FROM AUTHOR]

Slot jet reattachment (SJR) nozzle is a novel nozzle design that overperforms the regular impingement nozzles or perforated plates for heat transfer and drying, heating or cooling applications of moist materials. The SJR nozzle is a viable design to dry fragile products such as food and paper. Mesh-type conveyor belts are commonly used in the food industry. There is a need for understanding how the SJR nozzle performs with mesh-type conveyor belts because prior studies have solely considered solid surfaces. Therefore, an extensive numerical study is performed to solve the flow field of the SJR nozzle with an exit angle of +45° on top of a mesh-type belt with and without products using COMSOL Multiphysics® without including heat transfer. Turbulent fluid flow is theoretically modeled using the k-ɛ turbulence model for the SJR nozzle. The numerical predictions are validated with experimental data for stationary surface. Six different cases are studied to capture the effects of the presence of products on the belt and the conveyor belt speed. Additionally, the presence of an SJR nozzle on the bottom of the belt with and without a lateral offset is studied. The mass flow rate escaping underneath the belt is compared to the total mass flow rate at the nozzle exit, shear stresses are compared at three different surface velocities, and maximum force magnitudes as product travels are reported. The results show that the flow can reattach to the product surface regardless of surface motion, but reattachment characteristics depend on product orientation with respect to the SJR nozzle. Although significant mass flow escapes through the belt, the presence of products mitigates this loss. In addition, doubling the belt speed increases the shear stresses applied by the surface and decreases the average mass flow loss. Also, an additional SJR nozzle on the bottom of the belt almost cancels this mass flow loss even when there is a lateral offset between the nozzles. [ABSTRACT FROM AUTHOR]

Conveyor belts are widely used in ore transportation in large-scale mines for their long transportation range, high safety, and strong economic applicability. Coal mine belt conveyors are not only traditional, simple mechanical conveying devices but also automatic control system operating devices that integrate safety, stability, and low power consumption. In the process of coal mining, a conveyor belt control system also needs to be closely integrated with modern industrial systems and information systems, which greatly improves its work efficiency. The purpose of this article is to improve the methods for designing automatic control systems for electric motors in order to obtain mechanical characteristics close to a constant power line, which would ensure the reliable operation of belt conveyors. An automatic control system was designed based on the controller Siemens S7-1200; then, a mathematical model of an automated electric drive was developed. Based on the mathematical model, a simulation model of an automatic electric drive was built, and the modes were modeled. After designing, the obtained plots of transients completely corresponded to the required transients, which means that the Siemens frequency converter automatic control system (ACS) parameters were calculated quite accurately and the conveyor electric drive met all requirements. [ABSTRACT FROM AUTHOR]

In the current background of excessive usage of plastic and rubber materials, hardly degradable, when the pollution level and global warming problems due to industrialization are rising, the world turns to new materials, especially composites, with a low impact on the environment, and to using waste formed from other fabrication processes and which after the termination of their life cycle to be easily recycled and reused in the manufacturing of new products with very good qualities. [ABSTRACT FROM AUTHOR]

The paper presents the measured values of tensile forces acting on transmission idlers in the upper and lower run of a conveyor belt placed on a laboratory machine designed at the Department of Machine and Industrial Design, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava. The tensile forces detected by two strain gauge load cells and recorded using DEWESoft software were used to calculate the friction coefficient, which acts on the surface of the driving drum casing and the conveyor belt. The friction coefficient at the slip point, or during the slippage of the conveyor belt on the rubber or steel casing of the driving drum, was determined for two states of the surfaces that are in contact. Experimental measurements on a laboratory machine determined four values of friction coefficients for two types of drum surfaces and for two states of contact surfaces, which were compared with the recommended standard values. The measured values reached higher values in comparison with the values given by the CSN standards. The highest deviation of 273.3% is achieved using a steel wet surface, and the lowest deviation of 106.3% is achieved when using a rubber dry lining for the driving drum casing. On the presented laboratory machine, it is possible to measure tensile forces for different speeds of movement, different belt angles on the driving drum, various types of belt surfaces, different types of drum casing linings, and different sizes of tension forces for the endless loop of the conveyor belt. For these characteristics of the conveyor belt, the magnitude of the friction coefficient acting between the belt and the drum surfaces can be determined. [ABSTRACT FROM AUTHOR]